Lydia Marie DeWitt and Mary Butler Kirkbride: Prototypical Circa–Early-1900s Women of Pathology and an Analysis of Their Contributions to the Discovery of Insulin
Bibliographic record
Abstract
CONTEXT.—: The year 2023 marks the centenary of the Nobel Prize honoring the discovery of insulin. Little-known experimental pathologists Lydia DeWitt, MD, at the University of Michigan and Mary Kirkbride, DSc [Hon], at Columbia University, both just beginning their academic careers, made independent contributions to the discovery that have never been critically examined. This happened at a time when it was exceedingly rare for women to work in pathology. OBJECTIVE.—: To explore the facilitative roles of DeWitt and Kirkbride in the discovery of insulin and to examine their trail-breaking careers in academic pathology. DESIGN.—: Available primary and secondary historical resources were reviewed. RESULTS.—: DeWitt made and tested pancreatic extracts from duct-ligated atrophic pancreas (ie, Frederick Banting's great idea to prevent digestion of its hypothetical internal secretion) 15 years before Banting; Banting was unaware of her work. His idea came from reading a paper by pathologist Moses Barron. Prior duct-ligation studies had sometimes been viewed with skepticism because histologic identification of islets in atrophic duct-ligated pancreata was imperfect; Kirkbride addressed this with histochemical staining, convincing Barron and, therefore, indirectly influencing and motivating Banting. The lives and convoluted careers of these 2 early-20th-century women are explored and compared with those of other contemporary women in pathology. A unifying pattern becomes clear: careers in experimental pathology and bacteriology were accepted but performing clinical work in anatomic pathology was not. CONCLUSIONS.—: Both DeWitt and Kirkbride are prototypical early-20th-century women in academic pathology whose careers were constrained by gender. However, Kirkbride made a unique and unrecognized contribution to the discovery of insulin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".